Justice Owusu Agyemang

The paper systematically evaluates eight privacy-preserving techniques for LLM requests, finding that a combination of local inference, redaction, and semantic rephrasing provides the best overall protection.

This paper analyzes how vulnerable various machine learning models are to data poisoning attacks in IoT intrusion detection, finding that ensemble methods are more robust than Logistic Regression and Deep Neural Networks.

The paper introduces a geometry-aware framework for quantum differential privacy by aligning noise to the Quantum Fisher Information (QFI) eigenstructure, achieving significantly tighter privacy-utility bounds than classical methods.